Automatic control of complex machinery, such as moving vehicles exists, for example, the control systems for aircraft autopilots. Just as a man-machine interface is required for the man to control the machinery, an automation of the control system is largely specific to the particular machinery that is to be controlled. For example, pilots, even after extensive training on a particular aircraft, do hot qualify for piloting a similar aircraft, without extensive training on the alternate aircraft.
Agricultural machinery has become more expensive and complex to operate. Traditionally, human machine control has been limited to open-loop control design methods, where the human operator is assumed to receive appropriate feedback and perform adequate compensation to ensure that the machines function as required and to maintain stable operation. Design methods have included using an expert operator and fine-tuning the control with non-parametric feedback from the operator in terms of verbal cues. These approaches do not always translate to the best quantitative design or overall human-machine synergy.
Assuming that an individual expert operator is the only method of ensuring qualitative response presents several problems. One problem with this assumption is that humans are not the same, with varying perceptions, experience, reaction time, response characteristics and expectations from the machine. The result may be a perceived lack in the qualitative aspects of the human machine interface for some operators. The task of designing optimal human-machine system performance without a consistent operator becomes a daunting one, as there are no methods for setting appropriate constraints. Additionally, expert operators are themselves different in terms of level of efficiency, aggressiveness and sensitivity. Expert operators adapt vary quickly to machine designs, including inadequate ones. The result is that qualitative design change effectiveness is not guaranteed since they are applied based on an operator's continuously adapting perception of the machine performance.
What is needed is an autonomous control system for a dynamic-environment to address design issue variables including response fidelity, accuracy and noise from sensory information.